Cardiovascular signals contain parameters of clinical significance that must be estimated, but have a complicated nonlinear relationship to the observed signals. For instance, accurate estimation and tracking of the heart and respiratory frequencies from ABP, POX, and ICP is important for algorithms embedded in patient monitors in the emergency room and intensive care applications. Commercial monitoring systems often include the capability to monitor heart rate and several statistics of pressure signals such as the systolic, diastolic, and mean, but few can reliably estimate other components of pressure waveforms such as the respiratory rate, pulse pressure variation (PPV), harmonic phases, or pulse morphology.
Currently available systems are empirical in nature and do not used an underlying statistical model to estimates and automatically track all the cardiovascular parameters of interest and solve clinically important problems such as: 1) estimation and tracking of heart rate from pressure signals, 2) estimation and tracking of respiratory rate from pressure signals, 3) model-based filtering, artifact removal, and interpolation, 4) cardiovascular signal decomposition, characterization, and tracking of pulse morphology, and 5) PPV estimation on mechanically ventilated subjects during periods of abrupt hemodynamic monitoring
These problems have important clinical significance. For instance, accurate estimation and tracking of PPV is important, since numerous studies have found that PPV is one of the most sensitive and specific predictors of fluid responsiveness and PPV is used to optimize fluid therapy [1-6]. Characterization and tracking of the ICP pulse morphology during intracranial hypertension is also important. Several research studies have indicated ICP morphology changes correlate with a deterioration of the mechanisms that control ICP [7,8,8-11], and great interest exists in developing indices [8,10-14] to characterize and track the pulse morphology in order to understand how such changes in morphology are related to intracranial compliance, cerebral autoregulation (CAR), and outcome. Accurate tracking of the heart rate and respiratory rate from cardiovascular signals without the need for an automatic beat detection algorithm is also important. The ability to track heart rate without performing beat detection is significant since there are currently few publicly available detection algorithms for cardiovascular pressure signals such as ABP, ICP, and POX [15].